System for estimating unknown attributes of interest in the under-determined inverse problem and a process of accomplishing the same

ABSTRACT

A method for solving an under-determined inverse problem or network inference/tomography problem in per-flow size, delay, loss and throughput inference in a computer network, through a system is presented. The method includes the following steps, which are not necessarily in order. First, establishing the computer network having a plurality of nodes wherein the per-flow size, the delay, the loss and the throughput inference are unknown. An original observation or routing matrix determines how flows are appeared on the links and construct the measurements. Next, performing a learning phase to obtain an optimal observation matrix or pseudo-optimal observation matrix. After that, performing a computer controller adaptive measurement and inference phase to estimate the set of unknowns using the measurement quantities, and a function of one of the set consisting of: the optimal observation matrix, the original observation matrix, or both.

RELATED APPLICATION

This application is a divisional application of non-provisional patent application U.S. Ser. No. 15/002,376 filed on Jan. 20, 2016, which claims priority to provisional patent application U.S. Ser. No. 62/105,437 filed on Jan. 20, 2015, the entire contents of both applications is herein incorporated by reference.

BACKGROUND

The embodiments herein relate generally to systems that control flow in systems.

Prior to embodiments of the disclosed invention there was no efficient way to solve an under-determined inverse problem. As a result, there was no way to produce optimized flows in an underdetermined system. Embodiments of the disclosed invention solve this problem.

SUMMARY

A method for solving an under-determined inverse problem or network inference/tomography problem in per-flow size, delay, loss and throughput inference in a computer network, through a system given an original observation matrix, includes the following steps, which are not necessarily in order. First, establishing the computer network having a plurality of nodes wherein the per-flow size, the delay, the loss and the throughput inferences are unknown; wherein an original routing matrix determines how flows from each node to every other node are appeared on links. Next, performing a learning phase by first measuring a set of quantities which are combinations of unknowns creating a first set of measurements. Then, measuring at least some of a set of unknowns creating a second set of measurements. After that, using the first set of measurements and the second set of measurements, an optimal observation matrix or pseudo-optimal observation matrix is computed. Following that, performing a computer controller at least one member adaptive measurement and inference phase by first measuring the set of quantities which are the combinations of the unknowns creating measurement quantities. Then, identifying a partial set of most informative unknown network flows that must be re-measured and updated. After that, re-measuring a partial set of unknowns. Next, re-computing and updating the optimal observation matrix to form an updated optimal observation matrix. Following that, estimating the set of unknowns using the measurement quantities, and a function of one of the set consisting of: the optimal observation matrix, the original observation matrix, or both.

In some embodiments the method can include determining a starting time of the learning phase and a duration of the learning phase by detecting sequential significant changes in measurements during the adaptive measurement and inference phase. Then, determining a set of most informative unknown network flows using at least one learning algorithm; wherein the at least one learning algorithm includes a multi-bandit algorithm.

In some embodiments, the method can include generating the optimal observation matrix with a regularized or non-regularized optimization technique including a least square estimation technique with or without constraints.

In some embodiments, the under-determined inverse problem is a traffic matrix estimation problem and a set of unknown network flows are estimated by first, measuring SNMP link loads as measured quantities which are linear combinations of a size of unknown network flows. Then, measuring a partial set of unknown network flow sizes using counters associated with one of the set consisting of: flow-table entries, TCAM entries or both. Next, computing the optimal observation matrix as a replacement of the original routing matrix. After that, inferring a set of unknown flows using SNMP link load measurements, and using specific estimation techniques that can include a function of one of the set consisting of the optimal observation matrix, the original routing matrix, or both.

A method for determining a size of network flows in a computer network through a system that can include the following steps which are not necessarily in order. First, providing a network having a plurality of nodes connected by a plurality of links, wherein nodes denote network devices and/or elements including computer hosts, physical switches, physical routers, virtual switches, and virtual routers. Next, performing a learning phase by iteratively performing the following steps until all link loads and a set of unknown network flow sizes have been measured, and an optimal observation matrix is computed. First, creating a measured link load vector by performing, first, measuring a load on a set of the plurality of links creating a plurality of link load measurements where link loads are linear combinations of a set of unknown network flows. Then, forming the measured link load vector from the plurality of link load measurements. After that, transferring the measured link load vector to a network controller connected to the computer network. Following that, creating a vector representation of a measured traffic matrix by measuring and storing a size of a flow from each node to every other node for a specified time period, by performing the following steps. First, installing a set of rules in one of a set consisting of: a storage system, a memory system, or both of one of the set consisting of: physical/virtual network switches, physical/virtual network routers, or both. Next, polling counters in the storage system that create flow-size measurements as flows are measured and network packets are routed. After that, transferring the measurements into a memory, and the network controller. Following that, forming the optimal observation matrix by taking a matrix multiplication of the measured link load vector, a transpose of the vector representation of the measured traffic matrix, and a pseudo inverse of the matrix multiplication of the vector representation of the measured traffic matrix and the transpose of the vector representation of the measured traffic matrix.

In some embodiments this method can include, performing an adaptive measurement and inference phase by iteratively and adaptively performing the following steps until adjusted link loads are measured, a set of most informative unknown network flows to be re-measured and updated is identified, the set of most informative unknown network flows are measured and the vector representation of a traffic matrix is updated to form an adjusted vector representation of the traffic matrix, an adjusted optimal observation matrix is computed, and an unknown vector representation of the traffic matrix is estimated. The steps include: first, establishing an iteration time. Next, starting a timer. After that, for a timer value less than the iteration time, performing the following steps. First, creating an adjusted link load vector by performing the following steps. First, measuring an adjusted load on the set of the plurality of links creating a plurality of adjusted link load measurements. Next, forming the adjusted link load vector from the plurality of adjusted link load measurements. After that transferring the adjusted link load vector to the network controller. Following that, creating the adjusted vector representation of the traffic matrix by storing and updating an adjusted size of the flow from a set of nodes to another set of nodes identified by the set of most informative unknown network flows by identifying the set of most informative unknown network flows that must be re-measured and updated. Then, installing an adjusted set of rules, for measuring identified most informative flows, in one of a set consisting of: the storage system, the memory system, or both of one of the set consisting of: the network switches, the network routers, or both. In some embodiments, the network switches can be either physical switches, virtual switches or both. In some embodiments, the network routers can be either physical routers, virtual routers or both. After that, polling adjusted counters in the one of a set consisting of: the storage system and the memory system that create adjusted measurements of the most informative flows as adjusted flows are measured and the network packets are routed. Following that, transferring the adjusted measurements into the memory and the network controller. Then, forming the adjusted vector representation of the traffic matrix by updating the size of the most informative flows. Following that, forming the adjusted optimal observation matrix by taking the matrix multiplication of the adjusted link load vector, the transpose of the adjusted vector representation of the traffic matrix, and the pseudo inverse of the matrix multiplication of the adjusted vector representation of the traffic matrix and the transpose of the adjusted vector representation of the traffic matrix. After that, creating an estimation of the vector representation of the traffic matrix by taking the matrix multiplication of the pseudo inverse of the adjusted optimal observation matrix and the adjusted link load vector.

In some embodiments, this method can include, detecting a significant change in the measurements. Then, restarting the learning phase. Finally, identifying the set of the most informative flows by finding a set of flows which maximize an angle between a current SNMP link-load vector and a SNMP link-load vector at a time of the significant change. In some embodiments, the storage system can be one of the set consisting of: TCAM, flow-table entries, or both. The network controller can be connected to switches and routers on the network through one of the set consisting of: a secure channel, a protocol, or both.

In some embodiments, an under-determined inverse problem is an inverse problem wherein the number of measurements is less than the number of unknowns that must be estimated. An over-determined inverse problem is the inverse problem wherein the number of measurements is larger than the number of unknowns that must be estimated. A square inverse problem is an inverse problem wherein the number of measurements is equal to the number of unknowns that must be estimated.

BRIEF DESCRIPTION OF THE FIGURES

The detailed description of some embodiments of the invention is made below with reference to the accompanying figures, wherein like numerals represent corresponding parts of the figures.

FIG. 1 shows a schematic view of one embodiment of the present invention;

FIG. 2 shows a schematic view of one embodiment of the present invention;

FIG. 3 shows a schematic view of one embodiment of the present invention;

FIG. 4 shows a schematic view of one embodiment of the present invention;

FIG. 5A shows a schematic view of one embodiment of the present invention;

FIG. 5B shows a schematic view of one embodiment of the present invention;

FIG. 5C shows a schematic view of one embodiment of the present invention;

FIG. 5D shows a schematic view of one embodiment of the present invention;

FIG. 5E shows a schematic view of one embodiment of the present invention;

FIG. 6A shows a schematic view of one embodiment of the present invention;

FIG. 6B shows a schematic view of one embodiment of the present invention;

FIG. 6C shows a schematic view of one embodiment of the present invention;

FIG. 6D shows a schematic view of one embodiment of the present invention;

FIG. 6E shows a schematic view of one embodiment of the present invention;

FIG. 6F shows a schematic view of one embodiment of the present invention;

FIG. 7 shows a schematic view of one embodiment of the present invention;

FIG. 8 shows a schematic view of one embodiment of the present invention;

FIG. 9 shows a schematic view of one embodiment of the present invention;

FIG. 10 shows a graphical output of one embodiment of the present invention;

FIG. 11 shows a graphical output of one embodiment of the present invention;

DETAILED DESCRIPTION OF CERTAIN EMBODIMENTS

By way of example, and referring to FIG. 1, a controller 10 is communicatively coupled to an operating network 20 using a network communication system including network measurement controlling messages 14. Operating network 20 further comprises software defined network devices and non-software defined network devices. Network measurement controlling messages 14 can dynamically configure or reconfigure the software defined measurement network 12 as well as poll required network measurements and statistics 16. Operating network 20 and software defined measurement network 12 further comprise a plurality of network devices and elements including open flow switches 18, and simple network management protocol (SNMP) agents. Controller 10, which can either be a physical component or a virtualized controller, further comprises SNMP management system 36 and network configuration protocol. Software defined measurement network 12 further comprises a plurality of open flow switches 18A, 18B, 18C, 18D, and 18E in an operating network 20. The plurality of open flow switches 18A, 18B, 18C, 18D, and 18E are either mainly or partially for network traffic measurement and network packet forwarding and routing. In some embodiments, the plurality of open flow switches can be one of the set consisting of physical switches, virtual switches or both. Controller 10 further comprises an adaptive per flow measurement module 22 and an optimal-coherent inference module 24. The adaptive per flow measurement module 22 indicates the set of most informative flows 44 in the operating network 20 that can be directly measured. The optimal-coherent inference module 24 processes the measurements and statistics 16 and provides accurate estimates of network flows 42. The controller 10, then sends network measurement controlling messages 14 to the software defined measurement network 12. In some embodiments, required per flow measurements 32 and aggregated flow measurements 32 are measured by NetFlow protocol, sFlow protocol, and probing packets.

The network measurement controlling messages 14 indicate which flow in the operating network 20 should be directly measured at different times or locations by re-configuring either a ternary content addressable memory 26, a flow table or both. The ternary content addressable memory 26 (TCAM) models the TCAM entries of the plurality of open flow switches 18A, 18B, 18C, 18D, and 18E. The flexibility of open flow is used to install TCAM wildcard matching rules or prefix keys 28 and collect associated statistics 30. Associated statistics 30 can include per flow measurements 32 or aggregated measurement of multiple flows in each measurement interval 40. The longest prefix matching forwarding table mechanism in open flow switches 18 facilitates the implementation of a per-flow measurement process where prefix keys 28 of the per-flow measurements 34 are stored in the ternary content addressable memory 26 or flow tables and corresponding counters measure the size of flows. This process is called de-aggregation.

In some embodiments, controller 10 further comprises a SNMP management system 36, alternately, SNMP management system 36 is communicatively coupled to controller 10. Network measurement controlling messages 14 are a mean of the interaction of the Simple Network Management Protocol (SNMP) management system 36 and SNMP agents 38 to measure SNMP link-loads. In some embodiments, controller 10 and SNMP management system 36 are collocated in one machine and in other embodiments, they are located in separate machines and they can poll the statistic counts periodically or in different measurement intervals the frequency of which is limited by practical constraints. Regardless, a focus is on traffic matrix 52 measurement and inference where ternary content addressable memory 26 entries of all of the open flow switches 18 of the software defined measurement network 12 is modeled as a large flow table.

FIG. 2 shows another view of operating network 20. At each measurement interval 40 or r there are m SNMP link load measurements defined by the vector Y_(m×1) ^(t):={y_(i) ^(t)}_(i=1) ^(m) and n=N(N−1) flows which form the vector representation of traffic matrix 52, here X_(n×1) ^(t):={x_(j) ^(t)}_(j=1) ^(n). The SNMP link load vector 56 or Y^(t) is linearly related to the traffic matrix 52, here X^(t) as Y^(t)=HX^(t) where j^(th) 1 in i^(th) row of the binary routing matrix H represents the contribution of the j^(th) flow x_(j) ^(t) in the i^(th) link load y_(i) ^(t). If H is not binary, then j^(th) value in i^(th) row of matrix H represents the contribution of the j^(th) attribute x_(j) ^(t) in the i^(th) quantity y_(i) ^(t). The set of origin-destination flows in network exhibit a constant behavior in the sense that the dynamic of network flows 42 are predictive over time intervals with different durations, called as coherence time. The notion of coherence time is the time interval within which network flows 42 demonstrate, on average, a consistent and predictable behavior. Additionally, coherence time is defined as the time between the occurrences of two consecutive major changes in the behavior of the traffic matrix 52 which is quantified by an appropriate metric.

FIG. 3 represents the behavior of a network flow over multiple coherence times in the software defined measurement network 12. Change time t_(c) ^(l) indicates the time of the l^(th) major change in the network traffic and the l^(th) coherence time is the interval between l^(th) and (l+1)^(th) major change times in network flows 42. That is, T_(c) ^(l)={t_(c) ^(l)+1, . . . , t_(c) ^(l+1)−1}.

Controller 10 assumes that at the t^(th) measurement interval 40 with measurement interval 40, two sets of network measurements are provided. First the SNMP management system 36 interacts with SNMP agents 38 and provides the SNMP link load measurements Y^(t). Second, under hard resource constraint of the size of ternary content addressable memory 26 in flow tables of open flow switches 18A, 18B, 18C, 18E where the sum of all ternary content addressable memories in switches T<<n, all T entries in ternary content addressable memory 26 are used to track and measure all flows and/or the set of most informative flows 44A, 44B and 44C and to provide T per flow measurements 32.

Returning to FIG. 2, adaptive per flow measurement module 22 comprises two methods: change detection method 46 and adaptive traffic matrix updating method 48. At a first measurement interval 40 in the beginning of the learning phase 50, a first SNMP link-load vector Y^(t) ^(c) ^(l) is created. At t^(th) measurement interval 40, a second SNMP link-load vector Y^(t) is created. Additionally, the SNMP link-load vector Y^(t) is compared with the first SNMP link-load vector Y^(t) ^(c) ^(l) with change detection method 46. The detection of the l^(th) significant change in Y^(t)s indicates the beginning of a new coherence time and the beginning of a learning phase 50. At the new coherence time t_(c) ^(l), a learning phase 50 begins. In a learning phase 50, all available T entries of the ternary content addressable memory 26 in the software defined measurement network 12 are used to measure the most current traffic matrix 62. In a computer controlled measurement and inference phase 54, that is, over l^(th) coherence time T, the T most informative flows 44A, 44B and 44C are directly measured and T flows 42 are updated in the most current traffic matrix 62.

Controller 10 further utilizes the optimal-coherent inference module 24. Optimal-coherent inference module 24 uses the most current traffic matrix 62 and the SNMP link load vector 56 to identify an optimal observation matrix 58 of SNMP link load vectors 56, denoted by H_(O) ^(t). At each measurement interval 40, the per flow measurements 32 and the SNMP link-loads are used to coherently estimate the traffic matrix 60 or {circumflex over (X)}_(O) ^(t) (equivalently Ŵ_(O) ^(t)) using the following optimization formulation represented in the equation below where X_(τ0) _(t) ^(t) denotes the set of directly measured per-flow counts,

$X_{\overset{\_}{\tau\; 0^{t}}}^{t}$ denotes the set of flows that must be estimated, and

$X^{t} = {X_{\tau\; 0^{t}}^{t}\bigcup{X_{\overset{\_}{\tau\; 0^{t}}}^{t}.}}$

${\hat{W}}_{O}^{t} = {\min\limits_{X_{\overset{\_}{\tau\; 0^{t}}}^{t}}{X_{\overset{\_}{\tau\; 0^{t}}}^{t}}_{2}}$ s. t. X_(τ0) _(t) ^(t) is known and Y^(t)=H_(O) ^(t)X^(t) and

${X_{\tau\; 0^{t}}^{t}\bigcap X_{\overset{\_}{\tau\; 0^{t}}}^{t}} \neq \varnothing$

Under hard constraints of limited resources in software defined measurement network 12 this framework is simple, flexible and efficient with very high estimation accuracy which can be deployed on the plurality of open flow switches 18A, 18B, 18C, 18D, and 18E. Moreover, the communication overhead between controller 10, and the plurality of open flow switches 18A, 18B, 18C, 18D, and 18E is low because the required measurements for the fine-grained estimation of network flows 42 are aggregated SNMP link loads which are readily and reliably available in ISP networks. Further, only a few most beneficial flows are required to be directly measured.

FIG. 4 conceptually illustrates an electronic system 200 with which some embodiments of the invention are implemented. The electronic system 200 may be a computer, phone, PDA, or any other sort of electronic device. Such an electronic system includes various types of computer readable media and interfaces for various other types of computer readable media. Electronic system 200 includes a bus 205, processing unit(s) 210, a system memory 215, a read-only 220, a permanent storage device 225, input devices 230, output devices 235, a network 240, a controller 250, and measurement devices 260.

The bus 205 collectively represents all system, peripheral, and chipset buses that communicatively connect the numerous internal devices of the electronic system 200. For instance, the bus 205 communicatively connects the processing unit(s) 210 with the read-only 220, the system memory 215, and the permanent storage device 225.

From these various memory units, the processing unit(s) 210 retrieves instructions to execute and data to process in order to execute the processes of the invention. The processing unit(s) may be a single processor or a multi-core processor in different embodiments.

The read-only-memory (ROM) 220 stores static data and instructions that are needed by the processing unit(s) 210 and other modules of the electronic system. The permanent storage device 225, on the other hand, is a read-and-write memory device. This device is a non-volatile memory unit that stores instructions and data even when the electronic system 200 is off. Some embodiments of the invention use a mass-storage device (such as a magnetic or optical disk and its corresponding disk drive) as the permanent storage device 225.

Other embodiments use a removable storage device (such as a floppy disk or a flash drive) as the permanent storage device 225. Like the permanent storage device 225, the system memory 215 is a read-and-write memory device. However, unlike storage device 225, the system memory 215 is a volatile read-and-write memory, such as a random access memory. The system memory 215 stores some of the instructions and data that the processor needs at runtime. In some embodiments, the invention's processes are stored in the system memory 215, the permanent storage device 225, and/or the read-only 220. For example, the various memory units include instructions for processing appearance alterations of displayable characters in accordance with some embodiments. From these various memory units, the processing unit(s) 210 retrieves instructions to execute and data to process in order to execute the processes of some embodiments.

The bus 205 also connects to the input and output devices 230 and 235. The input devices enable the person to communicate information and select commands to the electronic system. The input devices 230 include alphanumeric keyboards and pointing devices (also called “cursor control devices”). The output devices 235 display images generated by the electronic system 200. The output devices 235 include printers and display devices, such as cathode ray tubes (CRT) or liquid crystal displays (LCD). Some embodiments include devices such as a touchscreen that functions as both input and output devices.

Bus 205 also couples electronic system 200 to a network 240 through a network adapter (not shown). In this manner, the computer can be a part of a communications network, or a network of computers (such as a local area network (“LAN”), a wide area network (“WAN”), or an intranet), or a network of networks (such as the Internet). Any or all components of electronic system 200 may be used in conjunction with the invention.

Bus 205 couples electronic system 200 to controller 250 and measurement devices 260. In some embodiments controller 250 can adaptively determine a set of required quantities and unknowns of interest that must be measured at important instances of time. Measurement devices 260 can be used to measure a set of required quantities and unknowns of interest.

These functions described above can be implemented in digital electronic circuitry, in computer software, firmware or hardware. The techniques can be implemented using one or more computer program products. Programmable processors and computers can be packaged or included in mobile devices or embedded systems. The processes may be performed by one or more programmable processors and by one or more set of programmable logic circuitry. General and special purpose computing and storage devices can be interconnected through communication networks.

Some embodiments include electronic components, such as microprocessors, storage and memory that store computer program instructions in a machine-readable or computer-readable medium (alternatively referred to as computer-readable storage media, machine-readable media, or machine-readable storage media). Some examples of such computer-readable media include RAM, ROM, read-only compact discs (CD-ROM), recordable compact discs (CD-R), rewritable compact discs (CD-RW), read-only digital versatile discs (e.g., DVD-ROM, dual-layer DVD-ROM), a variety of recordable/rewritable DVDs (e.g., DVD-RAM, DVD-RW, DVD+RW, etc.), flash memory (e.g., SD cards, mini-SD cards, micro-SD cards, etc.), magnetic and/or solid state hard drives, read-only and recordable Blu-Ray® discs, ultra density optical discs, any other optical or magnetic media, and floppy disks. The computer-readable media may store a computer program that is executable by at least one processing unit and includes sets of instructions for performing various operations. Examples of computer programs or computer code include machine code, such as is produced by a compiler, and files including higher-level code that are executed by a computer, an electronic component, or a microprocessor using an interpreter.

Optimal-coherent inference module 24 contains the following steps. First, at the l^(th) time change t_(c) ^(l), the optimal observation matrix 58 of SNMP link load vectors 56 is identified as H_(O) ^(t) ^(c) ^(l) . This can be done by first pooling the SNMP link load vectors 56 at t=t_(c) ^(l) using SNMP management system 36 and measuring the most current traffic matrix 62 at t=t_(c) ^(l). Next, a minimum norms solution is computed using the optimal observation matrix 58 for t=t_(c) ^(l)+1:1:t_(c) ^(l+1)−1. Both of these stages involve underdetermined inverse problems because all m×n elements of the optimal observation matrix 58 must be measured using m+n measurements of SNMP link load vector 56 and traffic matrix 52 at t=t_(c) ^(l) and m+n<m×n. In the second stage, m per-flow measurements 32 is less than n flows 42.

In Stage 1:

$H_{o}^{t} = {{\underset{H^{t}}{\arg\mspace{14mu}\min}{{Y^{t} - {H^{t}X^{t}}}}_{2}^{2}\mspace{14mu}{at}\mspace{14mu} t} = t_{c}^{l}}$

In Stage 2:

${\hat{W}}_{O}^{t} = {{\underset{X^{t}}{\arg\mspace{14mu}\min}{X^{t}}_{2}\mspace{14mu}{s.t.\mspace{14mu} Y^{t}}} = {{{H^{t}X^{t}\mspace{14mu}{for}\mspace{14mu} t} \in} = T_{c}^{l}}}$

In Stage 3: Ŵ_(O) ^(t)=H_(O) ^(t) ^(†) Y^(t) where H_(o) ^(t) ^(†) :=(H_(o) ^(t) ^(T) (H_(o) ^(t)H_(o) ^(t) ^(T) )⁻¹)

Where † indicates the pseudo-inverse operator, T indicates the complex conjugate transpose of the matrix, and Ŵ_(O) ^(t) denotes the estimate of the vector representation of the traffic matrix 52 or X^(t) at time t.

The optimal observation matrix 58 in Stage 1 can be computed as H_(o) ^(t)=Y^(t)X^(t) ^(T) (X^(t)X^(t) ^(T) )^(†). In some embodiments, the general form of optimal observation matrix 58 or H_(o) ^(t) can be used as H_(G) _(o) ^(t)=(Y^(t)X^(t) ^(T) (X^(t)X^(t) ^(T) )^(†)+QX^(⊥)) where Q is an arbitrary m×m matrix and X^(⊥) is the space of the rows perpendicular to X^(t). The inverse or pseudo-inverse of a matrix can be computed using different techniques including the singular value decomposition method.

In the learning phase, Minimum norm estimations using H_(o) ^(t)=Y^(t)X^(t) ^(T) (X^(t)X^(t) ^(T) )^(†) has several properties. First, estimations are identifiable, that is, Ŵ_(O) ^(t)=(H_(o) ^(t))^(†)Y^(t)=X^(t). Second, measurements are compatible, that is, Y^(t)=H_(o) ^(t)X^(t). Third, estimations are noise immune because even if measurement vector Y^(t) is noisy, then Ŵ_(O) ^(t)=(H_(o) ^(t))^(†)Y^(t)=X^(t).

In some embodiments, Ŵ_(O) ^(t) can be computed using both the original observation matrix H and the optimal observation matrix 58 or H_(o) ^(t), including the minimum-norm estimation technique as follows:

${\hat{W}}_{O}^{t} = {{\left( \begin{bmatrix} H \\ H_{o}^{t} \end{bmatrix} \right)^{\dagger}\begin{bmatrix} Y^{t} \\ Y^{t} \end{bmatrix}}.}$

In some embodiments, optimal-coherent inference module 24 can be alternately computed using regularized estimation techniques including the minimization of the addition of ∥Y^(t)−H^(t)X^(t)∥ and a fraction of ∥H^(t)∥. The unknown in this minimization problem that must be estimated is H^(t), and operator norm ∥.∥ is appropriately defined based on the application.

In some embodiments, computing the optimal observation matrix 506 can alternately include the following steps. Given a set of original observations, a set of new measurements is provided by designing the optimal observation matrix 506 which provides a set of most informative measurements to the set of given observations. This set of new observations is complementary to the set of given original observations and tries to minimize the estimation error as much as possible. For the application in network inference, let Y_(SS) be a set of T_(SS) vectors of original given link-loads Y^(t) where Y^(t) is an m×1 vector for t=1, . . . , T_(SS). At each time interval t, Y^(t)=HX^(t) where H is the original routing matrix with size m×n, and X^(t) is the vector representation of the traffic matrix 52. X^(t) is assumed to be known over T_(SS) periods of time t=1, . . . , T_(SS) in the learning phase 50. Then, a row of the optimal observation matrix 58 or H_(On) with n columns can be designed by maximizing distance d², defined in the equation below:

$h_{on} = {{\underset{h_{on}}{\arg\mspace{14mu}\max}d_{d}^{2}} = {\underset{h_{on}}{\arg\mspace{14mu}\max}\left( {{h_{i}X^{t}} - {h_{on}X^{t}}} \right)^{2}}}$ for i∈{1, . . . , m} and t∈{1, . . . , T_(SS)}

Here, h_(i) denotes the i^(th) row of matrix H, X^(t) denotes the t^(th) vector representation of the traffic matrix 52, and h_(on) denotes a row of matrix H_(on). This maximization can be performed, in an iterative way, using the least square estimation approach, including the Least Mean Squares (LMS) algorithm, defined in the equation below: h _(on)(j+1)=h _(on)(j)−2μdX ^(t) ^(T)

Here, there is iteration over parameter j until convergence. X^(t) ^(T) is the complex conjugate transpose of X^(t). μ is set to a small positive real number less than one. h_(on) is normalized by its norm ∥h_(on)∥ at each iteration j. Accordingly, by designing m₁ rows, an optimal observation matrix 58 or H_(On) with size m₁×n can be designed. A function of H_(on), denoted by H_(on) ^(f), can be used to collect a new set of measurements Y_(n) ^(t)=H_(on) ^(f)X^(t) where X^(t) is the unknown vector representation of the traffic matrix 52 for t>T_(SS). An estimate of X^(t), can be inferred by computing the matrix multiplication of the pseudo-inverse of H_(on) ^(f) and Y_(n) ^(t).

This process can include the following steps of learning phase 50. First, measuring a set of vector of quantities, which are combinations of unknowns, over several period of times and creating a first set of measurements. Next, measuring a set of vector of unknowns over several period of times and creating a second set of measurements. Finally, computing one of the set consisting of an optimal observation matrix 506 and pseudo-optimal observation matrix by using the first set of measurements and the second set of measurements, and the least square estimation approach, including the Least Mean Squares (LMS) algorithm.

FIG. 5A shows one embodiment of learning phase 50 in more detail. Learning phase 50 comprises the following steps, which are not necessarily in order. First, at step 502, measuring a set of quantities which are combinations of unknowns creating a first set of measurements. Next, at step 504, measuring at least some of a set of unknowns creating a second set of measurements. After that, at step 506, using the first set of measurements and the second set of measurements to form the optimal observation matrix 58 or pseudo-optimal observation matrix. In some embodiments, this can be done with a regularized optimization technique including a least square estimation technique with constraints. The regularized optimization technique can include the least square estimation technique without the constraints. Alternately, this can be done with a non-regularized optimization technique including a least square estimation technique with constraints. The non-regularized optimization technique can include the least square estimation technique without the constraints. Additional combinations of these can exist.

Turning to FIG. 5B, in some applications such as an under-determined multiple-input multiple output (MIMO) communication system, step 506 can be shown in more detail. In the learning phase 50, for all possible vectors of communication symbols, the optimal observation matrix 58 can be computed as follows. First, at step 5062, transmitting a vector of known symbols at multiple transmitters. Next, at step 5064, measuring the vector of known symbols at multiple receivers, and thereby creating vectors of measurements. After that, at step 5066, computing the optimal observation matrix 58 for each vector of known symbols creating a plurality of optimal observation matrixes 58. Finally, saving the vector of known symbols and the plurality of optimal observation matrix 58 at a receiver device.

Turning to FIG. 5C, learning phase 50 can be completed by performing the following steps. First, at step 508, creating a measured link load vector. Then, at step 510, creating a vector representation of a measured traffic matrix 52 by measuring and storing a size of a flow from each node to every other node for a specified time period. After that, at step 512, forming the optimal observation matrix 58 by taking a matrix multiplication of the measured link load vector, a transpose of the vector representation of the measured traffic matrix 52, and a pseudo inverse of the matrix multiplication of the vector representation of the measured traffic matrix 52 and the transpose of the vector representation of the measured traffic matrix 52.

Turning to FIG. 5D, in some embodiments, step 508—creating a measured link load vector can be accomplished with the following steps, which are not necessarily in order. First, at step 5082, measuring a load on a set of the plurality of links creating a plurality of link load measurements where link loads are linear combinations of unknown network flows 42. Next, at step 5084, forming the measured link load vector from the plurality of link load measurements. After that, at step 5086, transferring the measured link load vector to a network controller 10 that is connected to the computer communications network.

Turning to FIG. 5E, in some embodiments, step 510 creating a vector representation of a measured traffic matrix 52 by measuring and storing a size of a flow from each node to every other node for a specified time period can be accomplished with the following steps which are not necessarily in order. First, at step 5012, installing a set of rules in one of a set consisting of: a storage system, a memory system, or both of one of the set consisting of: network switches, network routers, or both. After that, at step 5014, polling counters in the storage system that create flow-size measurements as flows are measured and network packets are routed. Following that, at step 5016, transferring the measurements into a memory, and the network controller 10.

Turning to FIG. 6A, in some embodiments, computer controlled adaptive measurement and inference phase 54 includes the following steps, which are not necessarily in order. First, at step 542, measuring the set of quantities which are the combinations of the unknowns creating measurement quantities. Next, at step 544, identifying a partial set of most informative unknown network flows 42 that must be re-measured and updated. Following that, at step 546, re-measuring a partial set of unknowns. After that at step 548, re-computing and updating the optimal observation matrix 58 to form an updated optimal observation matrix 58. Then, at step 550, estimating the set of unknowns using the measurement quantities, and a function of one of the set consisting of: the optimal observation matrix 58, the original observation matrix, or both.

In FIG. 6B, step 5420, in some embodiments, the computer controlled measurement and inference phase 54 includes the following steps, which are not necessarily in order. First, at step 5422, transmitting an unknown vector of symbols from multiple transmitters. Next, at step 5424, creating a measurement vector from multiple measurements at multiple receivers. Following that, at step 5426, estimating vectors of symbols using the measurement vector and the optimal observation matrices creating estimated vectors of symbols. Then, at step 5428, computing a distance between the estimated vectors of symbols, and the vectors of known symbols. After that, at step 5430, decoding a minimum distance vector of known symbols as an ultimate estimate of the unknown vector of symbols.

In FIG. 6C, at step 550, estimating the set of unknowns using the measurement quantities, further includes the following steps which are not necessarily in order. First, at step 5502, measuring SNMP link loads as measured quantities which are linear combinations of a size of unknown network flows 42. Then, at step 5504, measuring a set of unknown network flow sizes 30 using counters associated with one of the set consisting of: flow-table entries, TCAM entries or both. After that, at step 5506, computing the optimal observation matrix 58 as a replacement of the original routing matrix. Following that, at step S508, a set of unknown flows are inferred using specific estimation techniques and using SNMP link load measurements, and the function of one of the set consisting of the optimal observation matrix 58, the original routing matrix, or both.

In FIG. 6D, one embodiment of computer controlled measurement and inference phase 54, includes the following steps, which are not necessarily in order. First, at step 552, establishing an iteration time. Next, at step 554, starting a timer. Following that, at step 556, for a timer value less than the iteration time, performing the following steps. If not, then stop the process at step 574.

If the process continues, at step 558, creating an adjusted link load vector. Then, at step 560, transmitting and storing the adjusted link load vector to the controller 10. After that, at step 562, detecting a significant change in the measurement quantities. If so, after that, at step 570, restarting the learning phase 50.

If not, then, at step 572, identifying a partial set of most informative flows 44. Following that, at step 576, measuring and storing a partial set of most informative flows by installing an adjusted set of rules and polling counters. Next, at step 578, transmitting adjusted measurements to the controller 10 and memory. After that at step 580, forming the adjusted optimal observation matrix 58 by taking the matrix multiplication of the adjusted link load vector, the transpose of the adjusted vector representation of the traffic matrix 52, and the pseudo inverse of the matrix multiplication of the adjusted vector representation of the traffic matrix 52 and the transpose of the adjusted vector representation of the traffic matrix 52. Following that, at step 582, creating an estimation of the vector representation of the traffic matrix 52 by taking the matrix multiplication of the pseudo inverse of the adjusted optimal observation matrix 58 and the adjusted link load vector.

In FIG. 6E, step 558, creating an adjusted link load vector, can include the following steps, which are not necessarily in order. First, at step 5582 measuring a load on a set of the plurality of links creating a plurality of link load measurements where link loads are linear combinations of unknown network flows 42. After that, at step 5584, forming the measured link load vector from the plurality of link load measurements. Following that, at step S586, transferring the measured link load vector to a network controller 10 connected to the computer network.

In FIG. 6F, step 585, creating the adjusted vector representation of the traffic matrix 52, can be accomplished with the following steps, which are not necessarily in order. First, at step 5822 installing a set of rules in one of a set consisting of: a storage system, a memory system, or both of one of the set consisting of: network switches, network routers, or both. Next, at step 5824, polling counters in the storage system that create flow-size measurements as flows are measured and network packets are routed. After that, at step 5826 transferring the measurements into a memory and the network controller 10.

The adaptive per flow measurement module 22 has two main components, the change detection method 46 and adaptive traffic matrix updating method 48. The change in the network traffic is measured by computing the angle between two SNMP link-load vectors, defined in the equation:

$\theta^{t} = {{\arccos\frac{\left\langle {Y^{t},Y^{t_{c}^{l}}} \right\rangle}{{Y^{t}}_{2}{Y^{t_{c}^{l}}}_{2}}\mspace{14mu}{where}\mspace{14mu} t} \in {{T_{c}^{l}\mspace{14mu}{and}\mspace{14mu} 0} \leq \theta^{t} \leq \frac{\pi}{2}}}$

Where Y^(t) and Y^(t) ^(c) ^(l) denote the SNMP link-load vectors at the beginning of the l^(th) coherence time and at the current time t, respectively. That is, θ^(t) quantifies the deviation of two SNMP link-load vectors at two different times Y^(t) and Y^(t) ^(c) ^(l) as an indication of a major change in the network traffics. Accordingly and to have a causal change detection method, at any time t, the average of θ^(t)'s over two periods of time, determined by two windows w_(t) ^(l) and w_(t) ^(r) with lengths L₁ and L_(r), is compared where w_(l) ^(t):={t−L₁−L_(r)+1, . . . , t−L_(r)} and w_(r) ^(t):={t−L_(r)+1, . . . , t}. The transition between two consecutive relative comparisons is reported as a change time t_(c) ^(l). Turning to FIG. 7, The pseudo-code of this process step 562, detecting a significant change in the measurement quantities is as follows:

-   -   Input: θ={θ^(t)}_(t=1) ^(t), w_(l) ^(t), w_(r) ^(t) at step 5622     -   Output: The set of detected change times S_(c) ^(t):={t_(c) ¹,         t_(c) ², . . . }     -   Initialization: l=0, S_(c) ^(t)=Ø     -   While t>L_(l)+L_(r)+1 do at step 5624

${E_{l}^{t} = {\frac{1}{L_{l}}{\sum\limits_{j = {t - L_{l} - L_{r} + 1}}^{t - L_{r}}\theta^{j}}}},{E_{r}^{t} = {\frac{1}{L_{r}}{\sum\limits_{j = {t - L_{r} + 1}}^{t}{\theta^{j}\mspace{14mu}{and}}}}}$ ${R_{E}^{t} = {{sign}\left( {\log\;\frac{E_{r}^{t}}{E_{l}^{t}}} \right)}},{T_{r}^{t} = {R_{E}^{t} - {R_{E}^{t - 1}\mspace{14mu}{at}\mspace{14mu}{step}\mspace{14mu} 5626}}}$

-   -   If there is a rise or fall between −1 and +1 in T_(r) ^(t) at         step 5628 then         -   l=l+1, t_(c) ^(l)=t−1, S_(c) ^(t)(l)=t_(c) ^(l) at step 5630     -   End if     -   End while

This method is not based on any statistical assumption about θ_(t), and thus, a more change detection method 46 can be used to enhance its performance. Without loss of generality and to simplify the current worst-case implementation of the change detection method 46, θ_(t) is computed as the angle between Y_(t) and Y₁ for t∈[2, . . . , T₀] where T₀ is the duration of traffic in each data set.

Turning to FIG. 8, to adaptively update the most current traffic matrix 62 or X_(mc) ^(t), which is required for the optimal-coherent inference module 24, the flexibility of the software defined measurement network 12 is used to allocate the limited available ternary content addressable memory 26 entries for measuring the most informative flows 44 that leads to the best possible estimation accuracy using the optimal-coherent inference module 24. The following method summarizes the adaptive traffic matrix updating method 48 where by detecting the l^(th) change time t_(c) ^(l) all T entries of ternary content addressable memory 26 are used to measure the X_(mc) ^(t) (:=X^(t) ^(c) ^(l) ) in one or more

$\left\lceil \frac{n}{T} \right\rceil$ measurement epochs; in this case, τ0^(t)={1, . . . , n} and |τ0^(t)|=n.

-   -   Input: number of TCAM entries T, l^(th) change time t_(c) ^(l),         (l+1)^(th) change time t_(c) ^(l+1) at step 472     -   Output: At each epoch t, the updated traffic matrix 52 or X_(mc)         ^(t) and τ0^(t)     -   Initialization: Use T entries of TCAM to measure X_(mc)         ^(t):=X_(mc) ^(t) ^(c) ^(l) at step 484     -   While t_(c) ^(l)+1≤t≤t_(c) ^(l+1), do at step 486         -   Find T entries of the updated traffic matrix 52 or X_(mc)             ^(t) indicated by the set τ0^(t), which maximizes the angle             between Y^(t) and Y^(t) ^(c) ^(l) .         -   Use T entries of the TCAM to measure the indicated flows at             the current time t as X_(τ0) _(t) ^(t)         -   X_(mc) ^(t)[τ0^(t)]=X_(τ0) _(t) ^(t), which in MATLAB             language means replacing all elements of X_(mc) ^(t) with             indices in the set τ0^(t) by X_(τ0) _(t) ^(t). End while

As shown in FIG. 9, for t_(c) ^(l)+1≤t≤t_(c) ^(l+1) the T flows 42 which maximize θ_(t) are directly measured. These per flow measurements 34 have the most contribution in equalizing/coinciding the two SNMP link-load vectors Y^(t) and Y^(t) ^(l) ^(c) ; in this case, τ0^(t)={i₁, . . . i_(j), . . . , i_(T)} where i_(j)∈{1, . . . , n} and |τ0^(t)|=T. Accordingly, at each epoch t, also, T elements of traffic matrix 52 vector X_(mc) ^(t) are updated by the recently measured flows 42. The following process represents the main steps in implementing the optimal-coherent inference module 24 in a sample coherence time T_(c) ^(l).

-   -   Input: Routing matrix H, # of TCAM entries T, T_(c) ^(l), X_(mc)         ^(t), τ0^(t) at step 242     -   Output: Ŵ_(o) ^(t) for tϵT_(c) ^(l)     -   For t from (t_(c) ^(l)+1) to (t_(c) ^(l+1)−1) with step size +1,         do at step 244         -   Ψ^(t)=X_(mc) ^(t)\X_(τ0) _(t) ^(t) and             ^(t)=Y^(t)−H(:,τ0^(t))X_(τ0) _(t) ^(t) where operator : is             the colon operator in MATLAB language and it means all rows,             and H(:,τ0^(t)) denotes a sub-matrix of H constructing from             all rows of H with columns identified by the set τ0^(t).         -   H_(o) ^(t)=             ^(t)Ψ^(t) ^(T) (Ψ^(t)Ψ^(t) ^(T) )^(†) and estimate Ψ*^(t) in             the same manner as Ŵ_(O) ^(t)=(H_(o) ^(t))^(†)             ^(t).     -   Ŵ_(o) ^(t)=Ψ*^(t)∪X_(τ0) _(t) ^(t)     -   End for

Here, to make the best use of the most-current X_(mc) ^(t), first, optimal-coherent inference module 24 cancels out the direct per-flow statistics 52, which have been accurately measured, and provide the required

^(t) and Ψ^(t) for optimal-coherent inference module 24. Then, the optimal observation matrix 58 of SNMP link-loads H_(o) ^(t) is computed. Finally, the minimum-norm solution Ψ*^(t) is estimated and traffic matrix 52 or Ŵ_(O) ^(t) at time t is constructed.

In all algorithms, the matrix and vector manipulations are match with the syntax of the commands of MATLAB language.

FIG. 8 and FIG. 9 represent the flowchart of algorithms in l^(th) coherence time T_(c) ^(l) from (T_(c) ^(l)+1) to (T_(c) ^(l+1)−1), that is, T_(c) ^(l)=({t_(c) ^(l)+1, . . . , t_(c) ^(l+1)−1}.

FIG. 10 and FIG. 11 show the effectiveness of embodiments of the disclosed invention with real traffic traces from Geant network considered. FIG. 10 shows the performance of a non-adaptive case which is a special case of the adaptive case with only one learning phase in the beginning. FIG. 11 shows the performance of an adaptive case where there are multiple learning phases at the important time of the detection of significant changes.

The framework below provides different metrics for evaluating the performance of the network controller 10.

${NMSE} = {{\frac{1}{T_{0}}{\sum\limits_{t = 1}^{T_{0}}{{{NMSE}(t)}\mspace{14mu}{where}\mspace{14mu}{{NMSE}(t)}}}}:=\frac{{{X^{t} - {\hat{W}}_{o}^{t}}}_{2}}{{X^{t}}_{2}}}$

NMSE denotes normalized mean squared error and measures the accuracy of flow size estimation technique. Accordingly, NMSE_(mn) denotes the error using the regular minimum norm estimation method where X_(mn) ^(t)=H^(†)Y^(t)=(H^(T)(HH^(T))⁻¹)Y^(t), and NMSE_(O) denotes the estimation error using the optimal-coherent inference module 24. The NMSE_(mn) ^(a), NMSE_(O) ^(a) denote the NMSE in the adaptive case, and NMSE_(mn) ^(na) an and NMSE_(O) ^(na) denote the NMSE in non-adaptive case.

As used in this application, the term “a” or “an” means “at least one” or “one or more.”

As used in this application, the term “about” or “approximately” refers to a range of values within plus or minus 10% of the specified number.

As used in this application, the term “substantially” means that the actual value is within about 10% of the actual desired value, particularly within about 5% of the actual desired value and especially within about 1% of the actual desired value of any variable, element or limit set forth herein.

All references throughout this application, for example patent documents including issued or granted patents or equivalents, patent application publications, and non-patent literature documents or other source material, are hereby incorporated by reference herein in their entireties, as though individually incorporated by reference, to the extent each reference is at least partially not inconsistent with the disclosure in the present application (for example, a reference that is partially inconsistent is incorporated by reference except for the partially inconsistent portion of the reference).

A portion of the disclosure of this patent document contains material which is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent file or records, but otherwise reserves all copyright rights whatsoever.

Any element in a claim that does not explicitly state “means for” performing a specified function, or “step for” performing a specified function, is not to be interpreted as a “means” or “step” clause as specified in 35 U.S.C. § 112, 6. In particular, any use of “step of” in the claims is not intended to invoke the provision of 35 U.S.C. § 112, 6.

While the foregoing written description of the invention enables one of ordinary skill to make and use what is considered presently to be the best mode thereof, those of ordinary skill will understand and appreciate the existence of variations, combinations, and equivalents of the specific embodiment, method, and examples herein. The invention should therefore not be limited by the above described embodiment, method, and examples, but by all embodiments and methods within the scope and spirit of the invention as claimed. 

What is claimed is:
 1. A method for determining unknowns of interest in under-determined inverse problems in a network through a system; the method comprising: (a) providing the network having a plurality of nodes connected through a plurality of links, wherein each path is a set of connected links; (b) measuring a set of measurements which are a combination of the unknowns of interest, creating a first set of measurements (c) forming an under-determined inverse problem using an original observation matrix and a vector representation of the unknowns of interest; (d) measuring a set of unknowns of interest, creating a second set of measurements; (e) computing an optimal observation matrix using a function of the first set of measurements and the second set of measurements; and (f) estimating the unknowns of interest using the optimal observation matrix, the first set of measurements and the second set of measurements; wherein, given the original observation matrix, an i^(th) row of the optimal observation matrix is iteratively computed using a Least Mean Square algorithm by: h _(oi)(j+1)=h _(oi)(j)−2μdX ^(T) wherein: j denotes an iteration index in the least mean square algorithm; h_(o1)(j) h_(oi)(j) is the it^(h) row of the optimal observation matrix; d is an error-term that can be calculated by computing a Euclidean distance between: 1) a cross product of the ith row of the original observation matrix and a vector of unknowns, and 2) the cross product of the ith row of the optimal observation matrix and the vector of unknowns; μ is a constant factor; T denotes one member of a variable set consisting of: a transpose and a complex-conjugate transpose; and X is the vector of unknowns that is available in a learning phase from different sources.
 2. The method of claim 1, wherein the optimal observation matrix is computed by a following equation: H _(o) =YX ^(T)(XX ^(T))^(†) wherein: H_(o) is the optimal observation matrix; Y is a column-wise vector representation of the first set of measurements, X is a column-wise vector representation of the second set of measurements; T denotes a transpose operator; and † denotes a pseudo inverse operator.
 3. The method of claim 1, wherein the optimal observation matrix is computed by a following equation: H _(o) =YX ^(T)(XX ^(T))^(†) wherein: H_(o) is the optimal observation matrix; Y is a column-wise vector representation of the first set of measurements; X^(t) is a column-wise vector representation of the second set of measurements; T denotes a complex conjugate transpose operator; and † denotes a pseudo inverse operator.
 4. The method of claim 1, wherein after computing the optimal observation matrix, an unknown of interest is estimated using another function of the optimal observation matrix and at least one member of a measurement set consisting of: the first set of measurements and the second set of measurements.
 5. The method of claim 1, wherein an unknown of interest is estimated by a following equation: {circumflex over (X)}=(H _(o))^(†) Y wherein: Y is a column-wise vector representation of the first set of measurements; (H_(o))^(†) is a pseudo-inverse of the optimal observation matrix; and {circumflex over (X)} is a column-wise vector representation of unknown of interests that are estimated.
 6. The method of claim 1, wherein the plurality of nodes is selected from a node set consists of: a physical network device, a physical network element, a virtual network device, a virtual network element, a computer host, a virtual machine, a physical network switch, a virtual network switch and a physical network router, a virtual network router, a physical network interface, and a virtual network interface.
 7. The method of claim 1, wherein the plurality of links are selected from a link set consists of: a physical link, a virtual link, a wired physical link, and a wireless physical link; and wherein the path is the set of connected links.
 8. The method of claim 1, wherein the first set of measurements is at least one member selected from a measurement set consisting of: at least one of network link-loads, at least one of network path-delays, at least one of network path-losses, and at least one of network path throughputs; wherein the set of unknowns of interest includes at least one member of an unknown set consisting of: at least one of flow-sizes, at least one of link-delays, at least one of link-losses, and at least one of link throughputs.
 9. The method of claim 1, wherein measurements of the set of unknowns of interest that creates the second set of measurements are approximated using: 1) mathematical, statistical, heuristic and experimental models, 2) auxiliary information, and 3) information from at least one member of an information set consisting of a network topology, a distance between nodes, a size of a queue, and a buffer in a node.
 10. The method of claim 1 wherein, measuring the first set of measurements, and the second set of measurements, an adjusted set of measurements and computing the optimal observation matrix are performed in both a learning phase and in a measurement and inference phase, and estimating the unknowns of interest are performed in the measurement and inference phase.
 11. The method of claim 1, wherein measuring the set of measurements further comprises adaptively measuring the set of measurements and adjusted measurements, in a learning phase and in a measurement and inference phase.
 12. The method of claim 1, wherein computing the optimal observation matrix further comprises adaptively computing the optimal observation matrix, in a learning phase and in a measurement and inference phase.
 13. The method of claim 1, wherein estimating the unknowns of interest further comprises adaptively estimating the unknowns of interest in a learning phase and in a measurement and inference phase.
 14. The method of claim 1, wherein nodes and at least one controller communicate via networking protocols for exchanging network measurements, controlling commands and management instructions.
 15. The method of claim 1, wherein the plurality of nodes are selected from a node set consists of: a network transceiver, a network transmitter, and a network receiver; wherein the first set of measurements are provided using at least one member of a measurement set consisting of: another function of inputs of network receivers, and another function of inputs of network transceivers; wherein the second set of measurements are provided using at least one member of another measurement set consisting of: another function of outputs of network transmitters, and another function of outputs of network transceivers; wherein the set of unknowns of interest are at least one member from a set consisting of: network transmitted information, and another function of network transmitted information. 